并排比较相同列但不同行的两个数据框

如何解决并排比较相同列但不同行的两个数据框

我有两个具有相同列标签的数据框，如下所示：

 df1 = {'key_1': {0: 'F',1: 'H',2: 'E'},'key_2': {0: 'F',1: 'G','min': {0: -158,1: -881,2: -674},'count': {0: 58,1: 24,2: 13}}
    
    df2 = {'key_1': {0: 'C',1: 'L',2: 'F',3: 'K'},'key_2': {0: 'C',1: 'D','min': {0: -452,1: -153,2: -181,3: -120},'count': {0: 7470,1: 1262,2: 171,3: 86}}

pandas.DataFrame.compare 可用于并排比较每列，但不适用于比较具有不同行的数据框

df1.compare(df2,keep_shape=True,keep_equal=True)

ValueError: 只能比较标记相同的 DataFrame 对象

我们可以使用 pandas.merge 实现相同的功能吗？

我在下面尝试过，但它没有对每个相应的列进行并排比较

pd.merge(df1,df2,on=['key_1','key_2'],suffixes=['_df1','_df2'],how='outer')

    key_1   key_2   min_df1  count_df1  min_df2 count_df2
0   F         F     -158.0   58.0     -181.0    171.0
1   H         G     -881.0   24.0      NaN      NaN
2   E         E     -674.0   13.0      NaN      NaN
3   C         C      NaN     NaN      -452.0    7470.0
4   L         D      NaN     NaN      -153.0    1262.0
5   K         K      NaN     NaN      -120.0    86.0

解决方法

使用 concat 并将 ['key_1','key_2'] 转换为 MultiIndex：

df = (pd.concat([df1.set_index(['key_1','key_2']),df2.set_index(['key_1','key_2'])],keys=['df1','df2'],axis=1)
        .sort_index(level=1,axis=1))
print (df)

              df1     df2    df1    df2
            count   count    min    min
key_1 key_2                            
C     C       NaN  7470.0    NaN -452.0
E     E      13.0     NaN -674.0    NaN
F     F      58.0   171.0 -158.0 -181.0
H     G      24.0     NaN -881.0    NaN
K     K       NaN    86.0    NaN -120.0
L     D       NaN  1262.0    NaN -153.0

合并后，您可以按字母顺序对列重新排序，以使它们并排：

first_columns = ['key_1','key_2']
merged_df = pd.merge(df1,df2,on=['key_1','key_2'],suffixes=['_df1','_df2'],how='outer')
merged_df = merged_df[first_columns + sorted([col for col in merged_df.columns if col not in first_columns ])]

一种方式：

merged_df = pd.merge(df1,suffixes=[
                     '_df1',how='outer').set_index(['key_1','key_2'])
merged_df.columns = merged_df.columns.str.split('_',expand=True)
merged_df = merged_df.sort_index(level=0,axis=1)